Project Description

Plasticity in the motor cortex is critical for the acquisition and maintenance of motor skills1. Here, structural remodeling of synapses is enhanced during experience-dependent learning and impaired during pathological changes associated with neurodegenerative diseases. For example, in wild-type mice repeated motor learning leads to clustered spine addition2, while in Parkinson’s disease (PD), characterized by a loss in midbrain dopaminergic neurons, structural and functional plasticity in the Motor Cortex (M1) is known to be impaired1. A locus of such plasticity related changes occurs on synapses located on both proximal and distal apical dendritic arbors of principal excitatory neurons in the cortex3. Yet, the exact circuit mechanisms regulating these plasticity related changes and how these specific synapses influence dendritic integration4,5 remains poorly understood. Here, we aim to map the electrical dynamics of dendrites in M1 of awake behaving mice and compare our findings to a mouse model of PD (α-synuclein (αSyn) preformed fibril (PFF) mouse synucleinopathy model). This will be achieved using a combination of two-photon targeted somatic whole-cell6 and nanopipette dendritic recordings7, two-photon calcium imaging, optogenetics and high-speed behavioral monitoring. To gain mechanistic insights we will use a spatial-light-holography (SLM) based two-photon uncaging platform to probe how stimulation of specific synaptic input patterns across the dendrite regulate plasticity and overall somatic gain in vitro, performed in both wild-type mice and the αSyn model. Eventually, our study will highlight how αSyn aggregation and prion-like propagation lead to time-dependent differences in branch-specific potentiation in neurons, cooperativity between synapses, and eventual somatic spike output.  

Start Date

August 2020

Postdoc Qualifications

The ideal candidate will have a PhD in the Biomedical Sciences including Neuroscience or other related fields, and have a strong computational and experimental background. Other relevant interests include experience with mouse physiology, electrophysiology, two-photon imaging and machine learning. The successful candidate will work in a multi-disciplinary setting and have a deep interest in using optical and electrical methods to dissect neural circuit mechanisms, and be an excellent communicator. 

Co-Advisors

Prof. Krishna Jayant (BME)
Prof. Jean-Christophe Rochet (MCMP)

References

Guo L., et al., Nature Neuroscience, 2015, 18, 9, 1299-1309

Fu, M., et al., Nature, 2012, 483(7387), p.92.

Murayama, M.; et al., Nature 2009, 457, 7233, 1137-1141.

Schiller, J.; et al., Nature 2000, 404, 6775, 285

Stuart, G. J.; Spruston, N. Nature neuroscience 2015, 18, 12, 1713-1721

Jayant, K.; et al, Cell Reports, 2019, 26, 1, 266-278

Jayant, K.; et al., Nature Nanotechnology 2017, 12, 335–342,